CDIPorto, Portugal
.NET & AI/ML Software Engineer
Hybride
📍Hybrid: 4x/week on-site
----------------------------
✅ The client is extremely selective, so we need to guarantee competences the best we can.
✅ We also aim to reduce the time spent doing the back and forth with questions for candidates to confirm their competences.
That's why candidates must also answer questions (in video format) here👇:
https://app.candidrecruit.com/apply/senior-net-ai-software-engineer
How to use:
- in the "First Name" textbox, candidates should use their first name
- in the "Last Name" textbox, candidates should use "LBC"
- all other details can be with random information 👍 (e.g: +351 000000, or ABC@gmail.com)
----------------------------
We are looking for a Senior .NET & AI/ML Software Engineer to join a Framework and Research team responsible for shared services and technical components used across several product areas.
You will combine C#/.NET software engineering with hands-on Machine Learning work. You will design backend services, create and deploy ML models, contribute to LLM-based solutions and help move AI projects from proof of concept to production.
The role requires strong experience across the full ML lifecycle, including data preparation, model training, evaluation, deployment, monitoring and continuous improvement.
Design, develop and maintain backend services using C# and .NET/.NET Core.
Translate business requirements into reliable software and AI/ML solutions.
Prepare datasets, define features and select suitable modelling approaches.
Build, train, fine-tune and evaluate different types of machine learning models.
Work with models such as classification, regression, NLP, time-series, anomaly detection, computer vision and deep learning.
Develop production ML pipelines using Python and frameworks such as PyTorch, TensorFlow or scikit-learn.
Deploy models through cloud endpoints, APIs, batch pipelines or edge applications.
Monitor model performance, data quality and service behaviour in production.
Apply model versioning, experiment tracking and, where required, retraining processes.
Develop LLM-based solutions using GPT, Transformers, Hugging Face, RAG and prompt engineering.
Create proofs of concept using tools such as Jupyter notebooks.
Design cloud-based architectures using Azure, AWS or equivalent services.
Use services such as Azure Machine Learning, Azure AI, AWS SageMaker, AWS Bedrock or Vertex AI.
Containerise applications and ML services with Docker and Kubernetes.
Contribute to CI/CD and MLOps practices.
Apply AI governance, security, privacy and guardrail principles.
Conduct code reviews, document technical decisions and mentor other engineers.
Work with Product Owners and cross-functional teams to deliver agreed outcomes.
Support production incidents, respect SLAs and identify technical debt.
Experience Required:
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or a related field.
At least 5 years of professional software engineering experience.
At least 5 years of hands-on experience with C# and .NET/.NET Core.
At least 3 years of hands-on experience in Machine Learning, AI Engineering or Data Science.
Strong Python skills for data processing, model development and ML pipelines.
Proven experience building, training and deploying at least 2 different types of ML models beyond LLM and RAG applications.
Practical experience with PyTorch, TensorFlow and/or scikit-learn.
Experience with model evaluation, experiment tracking, model versioning and production monitoring.
Experience deploying ML models through Azure, AWS or Google Cloud.
Knowledge of Azure ML, Azure AI, AWS SageMaker, AWS Bedrock, Vertex AI or similar services.
Experience with Docker, Kubernetes and CI/CD pipelines.
Knowledge of MLOps practices and production model operations.
Experience with NLP, LLMs, Transformers, Hugging Face or prompt engineering.
Good understanding of API security, AI governance and data privacy.
Experience with microservices, databases and high-volume backend systems.
Fluent English.
Experience working in Agile or Scrum teams.
Nice to have:
Experience with model drift, retraining and performance alerts.
Experience with feature stores, experiment tracking tools or model registries.
Experience with computer vision, recommender systems or anomaly detection.
Cloud or AI certifications.
Experience working with regulated data or strict privacy requirements.
Experience with Java and Spring Boot.
Contact:
costa.neto@littlebigconnection.com
+351 21 020 9908 (Tel. & WhatsApp)
Informations de la mission :
----------------------------
✅ The client is extremely selective, so we need to guarantee competences the best we can.
✅ We also aim to reduce the time spent doing the back and forth with questions for candidates to confirm their competences.
That's why candidates must also answer questions (in video format) here👇:
https://app.candidrecruit.com/apply/senior-net-ai-software-engineer
How to use:
- in the "First Name" textbox, candidates should use their first name
- in the "Last Name" textbox, candidates should use "LBC"
- all other details can be with random information 👍 (e.g: +351 000000, or ABC@gmail.com)
----------------------------
We are looking for a Senior .NET & AI/ML Software Engineer to join a Framework and Research team responsible for shared services and technical components used across several product areas.
You will combine C#/.NET software engineering with hands-on Machine Learning work. You will design backend services, create and deploy ML models, contribute to LLM-based solutions and help move AI projects from proof of concept to production.
The role requires strong experience across the full ML lifecycle, including data preparation, model training, evaluation, deployment, monitoring and continuous improvement.
Design, develop and maintain backend services using C# and .NET/.NET Core.
Translate business requirements into reliable software and AI/ML solutions.
Prepare datasets, define features and select suitable modelling approaches.
Build, train, fine-tune and evaluate different types of machine learning models.
Work with models such as classification, regression, NLP, time-series, anomaly detection, computer vision and deep learning.
Develop production ML pipelines using Python and frameworks such as PyTorch, TensorFlow or scikit-learn.
Deploy models through cloud endpoints, APIs, batch pipelines or edge applications.
Monitor model performance, data quality and service behaviour in production.
Apply model versioning, experiment tracking and, where required, retraining processes.
Develop LLM-based solutions using GPT, Transformers, Hugging Face, RAG and prompt engineering.
Create proofs of concept using tools such as Jupyter notebooks.
Design cloud-based architectures using Azure, AWS or equivalent services.
Use services such as Azure Machine Learning, Azure AI, AWS SageMaker, AWS Bedrock or Vertex AI.
Containerise applications and ML services with Docker and Kubernetes.
Contribute to CI/CD and MLOps practices.
Apply AI governance, security, privacy and guardrail principles.
Conduct code reviews, document technical decisions and mentor other engineers.
Work with Product Owners and cross-functional teams to deliver agreed outcomes.
Support production incidents, respect SLAs and identify technical debt.
Experience Required:
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or a related field.
At least 5 years of professional software engineering experience.
At least 5 years of hands-on experience with C# and .NET/.NET Core.
At least 3 years of hands-on experience in Machine Learning, AI Engineering or Data Science.
Strong Python skills for data processing, model development and ML pipelines.
Proven experience building, training and deploying at least 2 different types of ML models beyond LLM and RAG applications.
Practical experience with PyTorch, TensorFlow and/or scikit-learn.
Experience with model evaluation, experiment tracking, model versioning and production monitoring.
Experience deploying ML models through Azure, AWS or Google Cloud.
Knowledge of Azure ML, Azure AI, AWS SageMaker, AWS Bedrock, Vertex AI or similar services.
Experience with Docker, Kubernetes and CI/CD pipelines.
Knowledge of MLOps practices and production model operations.
Experience with NLP, LLMs, Transformers, Hugging Face or prompt engineering.
Good understanding of API security, AI governance and data privacy.
Experience with microservices, databases and high-volume backend systems.
Fluent English.
Experience working in Agile or Scrum teams.
Nice to have:
Experience with model drift, retraining and performance alerts.
Experience with feature stores, experiment tracking tools or model registries.
Experience with computer vision, recommender systems or anomaly detection.
Cloud or AI certifications.
Experience working with regulated data or strict privacy requirements.
Experience with Java and Spring Boot.
Contact:
costa.neto@littlebigconnection.com
+351 21 020 9908 (Tel. & WhatsApp)
Informations de la mission :
- Début de mission : 01/08/2026
- Durée : 6 mois
- Date de publication : 08/09/2026
- Date limite de candidature : 20/10/2026
Compétences recherchées
C# (Indispensable).NET (Indispensable).NET Core (Indispensable)Python (Indispensable)PyTorch (Indispensable)TensorFlow (Indispensable)scikit-learn (Indispensable)LLM (Indispensable)Transformers (Indispensable)Hugging Face (Indispensable)RAG (Indispensable)prompt engineering (Indispensable)Azure (Indispensable)AWS (Indispensable)Azure Machine Learning (Indispensable)Azure AI (Indispensable)AWS SageMaker (Indispensable)AWS Bedrock (Indispensable)Vertex AI (Indispensable)Docker (Indispensable)Kubernetes (Indispensable)CI/CD (Indispensable)MLOps (Indispensable)Jupyter (Indispensable)GPT (Indispensable)microservices (Indispensable)databases (Indispensable)NLP (Indispensable)computer vision (Indispensable)anomaly detection (Indispensable)time-series (Indispensable)classification (Indispensable)regression (Indispensable)API security (Indispensable)feature stores (Indispensable)experiment tracking (Indispensable)model registries (Indispensable)AI security (Apprécié)governance and data privacy (Indispensable)Docker and Kubernetes (Indispensable)Microservices and database design (Indispensable)Machine Learning model development and deployment (Indispensable)Model evaluation (Apprécié)monitoring and versioning (Indispensable)C# and .NET/.NET Core (Indispensable)Cloud ML platforms (Apprécié)Azure or AWS (Indispensable)Python and ML frameworks (Indispensable)MLOps and CI/CD (Indispensable)LLMs (Apprécié)Transformers and Hugging Face (Indispensable)
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